Signal processing is a very useful field of study in the interpretation of signals in many
everyday applications. In the case of applications with time-varying signals, one possibility is …

Graph signal processing has recently received considerable attention. Several concepts,
tools, and applications in signal processing such as filtering, transforming, and sampling …

The analysis of sensor networks for air pollution monitoring is challenging. Recent studies
have demonstrated the ability to reconstruct the network measurements with graphs derived …

Existing air pollution monitoring networks use reference stations as the main nodes. The
addition of low-cost sensors calibrated in-situ with machine learning techniques allows the …

Air pollution monitoring platforms play a very important role in preventing and mitigating the
effects of pollution. Recent advances in the field of graph signal processing have made it …

Graph signal processing (GSP) facilitates the analysis of high-dimensional data on non-
Euclidean domains by utilizing graph signals defined on graph vertices. In addition to static …

Many modern applications make use of 3D modeling and/or reconstruction of complex
objects, such as historical monuments and entire urban centers. One of the most common …

In this paper, we employ a graph signal processing approach to redefine Fourier-like
number-theoretic transforms, which includes the Fourier number transform itself, the Hartley …

The lifetime of an Internet of Things (IoT) system consisting of battery-powered devices can
be increased by minimizing the number of transmissions per device while not excessively …

Complex structured data-driven applications frequently encompass a higher-order
connectivity or interaction among data samples and can be represented by a multilayer …